Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations649
Missing cells176
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.9 KiB
Average record size in memory88.2 B

Variable types

Numeric8
DateTime1
Categorical2

Alerts

period has constant value "2025-09-30 00:00:00"Constant
ZCTA5CE is highly overall correlated with metro_key and 2 other fieldsHigh correlation
commute_min_proxy is highly overall correlated with long45_share and 1 other fieldsHigh correlation
long45_share is highly overall correlated with commute_min_proxy and 1 other fieldsHigh correlation
long60_share is highly overall correlated with commute_min_proxy and 1 other fieldsHigh correlation
metro_key is highly overall correlated with ZCTA5CE and 1 other fieldsHigh correlation
metro_name is highly overall correlated with ZCTA5CE and 1 other fieldsHigh correlation
zori is highly overall correlated with ZCTA5CEHigh correlation
rent_to_income has 12 (1.8%) missing valuesMissing
period has 76 (11.7%) missing valuesMissing
zori has 76 (11.7%) missing valuesMissing
ZCTA5CE has unique valuesUnique
stops_per_km2 has 140 (21.6%) zerosZeros

Reproduction

Analysis started2025-11-07 02:13:13.460859
Analysis finished2025-11-07 02:13:25.116854
Duration11.66 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

ZCTA5CE
Real number (ℝ)

High correlation  Unique 

Distinct649
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81865.33
Minimum38002
Maximum91803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:25.255656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum38002
5-th percentile38130
Q176018
median85268
Q390403
95-th percentile91664.4
Maximum91803
Range53801
Interquartile range (IQR)14385

Descriptive statistics

Standard deviation12762.845
Coefficient of variation (CV)0.15590049
Kurtosis5.5100464
Mean81865.33
Median Absolute Deviation (MAD)5840
Skewness-2.3226604
Sum53130599
Variance1.628902 × 108
MonotonicityNot monotonic
2025-11-06T20:13:25.442695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
762661
 
0.2%
850031
 
0.2%
850041
 
0.2%
850061
 
0.2%
850071
 
0.2%
850081
 
0.2%
850091
 
0.2%
850121
 
0.2%
850131
 
0.2%
761821
 
0.2%
Other values (639)639
98.5%
ValueCountFrequency (%)
380021
0.2%
380161
0.2%
380171
0.2%
380181
0.2%
380281
0.2%
380491
0.2%
380531
0.2%
380571
0.2%
380601
0.2%
380661
0.2%
ValueCountFrequency (%)
918031
0.2%
918011
0.2%
917921
0.2%
917911
0.2%
917901
0.2%
917891
0.2%
917801
0.2%
917761
0.2%
917751
0.2%
917731
0.2%

rent_to_income
Real number (ℝ)

Missing 

Distinct637
Distinct (%)100.0%
Missing12
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean0.25975441
Minimum0.085952761
Maximum0.58021153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:25.610826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.085952761
5-th percentile0.1760312
Q10.2255127
median0.25580122
Q30.28992115
95-th percentile0.36056853
Maximum0.58021153
Range0.49425877
Interquartile range (IQR)0.064408458

Descriptive statistics

Standard deviation0.057873004
Coefficient of variation (CV)0.22279893
Kurtosis2.6327244
Mean0.25975441
Median Absolute Deviation (MAD)0.032643909
Skewness0.81659924
Sum165.46356
Variance0.0033492846
MonotonicityNot monotonic
2025-11-06T20:13:25.980192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.24399861981
 
0.2%
0.25580121541
 
0.2%
0.26454774921
 
0.2%
0.30426911931
 
0.2%
0.22596020571
 
0.2%
0.22536340061
 
0.2%
0.22795716061
 
0.2%
0.30645394911
 
0.2%
0.2324140821
 
0.2%
0.30305866191
 
0.2%
Other values (627)627
96.6%
(Missing)12
 
1.8%
ValueCountFrequency (%)
0.08595276151
0.2%
0.11625794731
0.2%
0.11849765581
0.2%
0.12509142081
0.2%
0.12617142861
0.2%
0.12721101441
0.2%
0.13276089831
0.2%
0.13749868031
0.2%
0.13812772081
0.2%
0.13877159011
0.2%
ValueCountFrequency (%)
0.58021152661
0.2%
0.51754413161
0.2%
0.49274002821
0.2%
0.44977770471
0.2%
0.43253120061
0.2%
0.43185973851
0.2%
0.43176585371
0.2%
0.4306991021
0.2%
0.42968188431
0.2%
0.42362940241
0.2%

long45_share
Real number (ℝ)

High correlation 

Distinct645
Distinct (%)100.0%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.1666197
Minimum0.022607781
Maximum0.39450652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:26.152638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.022607781
5-th percentile0.061826483
Q10.12264362
median0.16671268
Q30.20398595
95-th percentile0.27543128
Maximum0.39450652
Range0.37189874
Interquartile range (IQR)0.081342331

Descriptive statistics

Standard deviation0.063323871
Coefficient of variation (CV)0.38005032
Kurtosis0.40140728
Mean0.1666197
Median Absolute Deviation (MAD)0.041082278
Skewness0.3468583
Sum107.46971
Variance0.0040099126
MonotonicityNot monotonic
2025-11-06T20:13:26.320136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14185933091
 
0.2%
0.13814377511
 
0.2%
0.14627847131
 
0.2%
0.18912138091
 
0.2%
0.10255436371
 
0.2%
0.18449828931
 
0.2%
0.076328113021
 
0.2%
0.083737763651
 
0.2%
0.069858451391
 
0.2%
0.10477475581
 
0.2%
Other values (635)635
97.8%
(Missing)4
 
0.6%
ValueCountFrequency (%)
0.022607781281
0.2%
0.027706033521
0.2%
0.029947534491
0.2%
0.033238522151
0.2%
0.039438208541
0.2%
0.039956803461
0.2%
0.042552618031
0.2%
0.043132269811
0.2%
0.045243578011
0.2%
0.047385823541
0.2%
ValueCountFrequency (%)
0.39450651771
0.2%
0.37620076391
0.2%
0.37539306821
0.2%
0.36887115171
0.2%
0.36546695631
0.2%
0.35838607591
0.2%
0.3433734941
0.2%
0.34292565951
0.2%
0.33821354691
0.2%
0.33064979871
0.2%

long60_share
Real number (ℝ)

High correlation 

Distinct645
Distinct (%)100.0%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.1191971
Minimum0.012438749
Maximum0.30537975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:26.492737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.012438749
5-th percentile0.043996025
Q10.081910473
median0.1175274
Q30.14950634
95-th percentile0.20996266
Maximum0.30537975
Range0.292941
Interquartile range (IQR)0.06759587

Descriptive statistics

Standard deviation0.050610937
Coefficient of variation (CV)0.42459873
Kurtosis0.77031141
Mean0.1191971
Median Absolute Deviation (MAD)0.033272769
Skewness0.60160955
Sum76.88213
Variance0.002561467
MonotonicityNot monotonic
2025-11-06T20:13:26.660696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11088495841
 
0.2%
0.097859386151
 
0.2%
0.11306139321
 
0.2%
0.1237381231
 
0.2%
0.074497862451
 
0.2%
0.14129132641
 
0.2%
0.052985969741
 
0.2%
0.047596158861
 
0.2%
0.055543276951
 
0.2%
0.064676435211
 
0.2%
Other values (635)635
97.8%
(Missing)4
 
0.6%
ValueCountFrequency (%)
0.012438748591
0.2%
0.015772870661
0.2%
0.017971823151
0.2%
0.020039012331
0.2%
0.020877762871
0.2%
0.021038453331
0.2%
0.024274937891
0.2%
0.026723722031
0.2%
0.026997840171
0.2%
0.030318444291
0.2%
ValueCountFrequency (%)
0.30537974681
0.2%
0.30335777691
0.2%
0.29844738361
0.2%
0.29729360651
0.2%
0.28919759021
0.2%
0.27897258331
0.2%
0.27546244421
0.2%
0.26618705041
0.2%
0.26551853461
0.2%
0.25294782811
0.2%

commute_min_proxy
Real number (ℝ)

High correlation 

Distinct645
Distinct (%)100.0%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean11.998339
Minimum1.5383807
Maximum28.313399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:26.827966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5383807
5-th percentile4.5678833
Q18.7316803
median12.02484
Q314.922152
95-th percentile19.963792
Maximum28.313399
Range26.775018
Interquartile range (IQR)6.1904713

Descriptive statistics

Standard deviation4.6617326
Coefficient of variation (CV)0.38853148
Kurtosis0.41204218
Mean11.998339
Median Absolute Deviation (MAD)3.0109711
Skewness0.40640962
Sum7738.9289
Variance21.73175
MonotonicityNot monotonic
2025-11-06T20:13:26.998986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.621108411
 
0.2%
9.9869777911
 
0.2%
10.477764291
 
0.2%
12.936467661
 
0.2%
7.3415291791
 
0.2%
13.385351721
 
0.2%
5.7179239711
 
0.2%
5.9719262321
 
0.2%
5.1613798221
 
0.2%
7.3093929281
 
0.2%
Other values (635)635
97.8%
(Missing)4
 
0.6%
ValueCountFrequency (%)
1.5383806521
0.2%
2.001133321
0.2%
2.0598691751
0.2%
2.4414228441
0.2%
2.6987041041
0.2%
2.8092276481
0.2%
2.8341500191
0.2%
2.9716121441
0.2%
3.360472431
0.2%
3.3950326181
0.2%
ValueCountFrequency (%)
28.31339851
0.2%
27.84991931
0.2%
27.596116411
0.2%
26.945411391
0.2%
26.372322981
0.2%
26.352759551
0.2%
26.302649921
0.2%
24.633892891
0.2%
24.485746861
0.2%
23.564257031
0.2%

ttw_total
Real number (ℝ)

Distinct639
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13333.213
Minimum0
Maximum44521
Zeros4
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:27.169158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1656.6
Q17229
median12618
Q317946
95-th percentile28047.8
Maximum44521
Range44521
Interquartile range (IQR)10717

Descriptive statistics

Standard deviation8172.9074
Coefficient of variation (CV)0.6129736
Kurtosis0.68395065
Mean13333.213
Median Absolute Deviation (MAD)5386
Skewness0.74992498
Sum8653255
Variance66796415
MonotonicityNot monotonic
2025-11-06T20:13:27.332648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
0.6%
179462
 
0.3%
95542
 
0.3%
89492
 
0.3%
255832
 
0.3%
12512
 
0.3%
28602
 
0.3%
184342
 
0.3%
294111
 
0.2%
188791
 
0.2%
Other values (629)629
96.9%
ValueCountFrequency (%)
04
0.6%
991
 
0.2%
3311
 
0.2%
3641
 
0.2%
4121
 
0.2%
7661
 
0.2%
8031
 
0.2%
8291
 
0.2%
8591
 
0.2%
9261
 
0.2%
ValueCountFrequency (%)
445211
0.2%
424701
0.2%
410601
0.2%
407641
0.2%
394551
0.2%
392581
0.2%
385331
0.2%
372431
0.2%
371921
0.2%
364741
0.2%

period
Date

Constant  Missing 

Distinct1
Distinct (%)0.2%
Missing76
Missing (%)11.7%
Memory size5.2 KiB
Minimum2025-09-30 00:00:00
Maximum2025-09-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-06T20:13:27.452664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:27.550638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

zori
Real number (ℝ)

High correlation  Missing 

Distinct573
Distinct (%)100.0%
Missing76
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean2335.3887
Minimum917.72938
Maximum12446.389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:27.694157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum917.72938
5-th percentile1293.8449
Q11671.7149
median2137.0833
Q32755.4559
95-th percentile3743.1624
Maximum12446.389
Range11528.66
Interquartile range (IQR)1083.741

Descriptive statistics

Standard deviation1097.5023
Coefficient of variation (CV)0.46994415
Kurtosis29.197347
Mean2335.3887
Median Absolute Deviation (MAD)520.61208
Skewness4.1931582
Sum1338177.7
Variance1204511.2
MonotonicityNot monotonic
2025-11-06T20:13:27.872648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.6956281
 
0.2%
1450.0026461
 
0.2%
1457.8584831
 
0.2%
1538.6111111
 
0.2%
1580.3891671
 
0.2%
1609.6260881
 
0.2%
1545.9619241
 
0.2%
1253.5443581
 
0.2%
1699.2939131
 
0.2%
1674.9333331
 
0.2%
Other values (563)563
86.7%
(Missing)76
 
11.7%
ValueCountFrequency (%)
917.72937711
0.2%
951.16666671
0.2%
1002.3611111
0.2%
1056.5464051
0.2%
1075.1960781
0.2%
1095.9369411
0.2%
1108.751
0.2%
1122.2182541
0.2%
1146.3657741
0.2%
1152.8846151
0.2%
ValueCountFrequency (%)
12446.388891
0.2%
11651.666671
0.2%
10191.515871
0.2%
8495.7222221
0.2%
8149.5158731
0.2%
7887.9166671
0.2%
6866.6666671
0.2%
59501
0.2%
5577.751
0.2%
4928.7407411
0.2%

stops_per_km2
Real number (ℝ)

Zeros 

Distinct510
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5197672
Minimum0
Maximum53.879431
Zeros140
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-11-06T20:13:28.040640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02087042
median1.1672943
Q34.9078713
95-th percentile12.879522
Maximum53.879431
Range53.879431
Interquartile range (IQR)4.8870009

Descriptive statistics

Standard deviation5.7939228
Coefficient of variation (CV)1.6461097
Kurtosis20.015091
Mean3.5197672
Median Absolute Deviation (MAD)1.1672943
Skewness3.6574696
Sum2284.3289
Variance33.569541
MonotonicityNot monotonic
2025-11-06T20:13:28.232985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0140
 
21.6%
6.1279553341
 
0.2%
5.6579464591
 
0.2%
16.703534961
 
0.2%
13.120349021
 
0.2%
12.362611711
 
0.2%
10.399347381
 
0.2%
7.1524699431
 
0.2%
8.7878952021
 
0.2%
3.4326001051
 
0.2%
Other values (500)500
77.0%
ValueCountFrequency (%)
0140
21.6%
0.00043149735971
 
0.2%
0.0010790772391
 
0.2%
0.0014913787191
 
0.2%
0.0016726539021
 
0.2%
0.0018033753521
 
0.2%
0.0021369522911
 
0.2%
0.0026603136481
 
0.2%
0.0031901009581
 
0.2%
0.0033461687631
 
0.2%
ValueCountFrequency (%)
53.879431211
0.2%
46.419683921
0.2%
41.968330661
0.2%
35.304012861
0.2%
34.028340091
0.2%
31.438624451
0.2%
30.611613481
0.2%
25.153465121
0.2%
24.344203561
0.2%
23.076520511
0.2%

metro_key
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
los_angeles
270 
dallas
190 
phoenix
150 
memphis
39 

Length

Max length11
Median length7
Mean length8.3713405
Min length6

Characters and Unicode

Total characters5433
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowphoenix
2nd rowphoenix
3rd rowphoenix
4th rowphoenix
5th rowphoenix

Common Values

ValueCountFrequency (%)
los_angeles270
41.6%
dallas190
29.3%
phoenix150
23.1%
memphis39
 
6.0%

Length

2025-11-06T20:13:28.395998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-06T20:13:28.522685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
los_angeles270
41.6%
dallas190
29.3%
phoenix150
23.1%
memphis39
 
6.0%

Most occurring characters

ValueCountFrequency (%)
l920
16.9%
s769
14.2%
e729
13.4%
a650
12.0%
n420
7.7%
o420
7.7%
_270
 
5.0%
g270
 
5.0%
d190
 
3.5%
p189
 
3.5%
Other values (4)606
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l920
16.9%
s769
14.2%
e729
13.4%
a650
12.0%
n420
7.7%
o420
7.7%
_270
 
5.0%
g270
 
5.0%
d190
 
3.5%
p189
 
3.5%
Other values (4)606
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l920
16.9%
s769
14.2%
e729
13.4%
a650
12.0%
n420
7.7%
o420
7.7%
_270
 
5.0%
g270
 
5.0%
d190
 
3.5%
p189
 
3.5%
Other values (4)606
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l920
16.9%
s769
14.2%
e729
13.4%
a650
12.0%
n420
7.7%
o420
7.7%
_270
 
5.0%
g270
 
5.0%
d190
 
3.5%
p189
 
3.5%
Other values (4)606
11.2%

metro_name
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Los Angeles-Long Beach-Anaheim, CA
270 
Dallas-Fort Worth-Arlington, TX
190 
Phoenix-Mesa-Chandler, AZ
150 
Memphis, TN-MS-AR
39 

Length

Max length34
Median length31
Mean length30.020031
Min length17

Characters and Unicode

Total characters19483
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix-Mesa-Chandler, AZ
2nd rowPhoenix-Mesa-Chandler, AZ
3rd rowPhoenix-Mesa-Chandler, AZ
4th rowPhoenix-Mesa-Chandler, AZ
5th rowPhoenix-Mesa-Chandler, AZ

Common Values

ValueCountFrequency (%)
Los Angeles-Long Beach-Anaheim, CA270
41.6%
Dallas-Fort Worth-Arlington, TX190
29.3%
Phoenix-Mesa-Chandler, AZ150
23.1%
Memphis, TN-MS-AR39
 
6.0%

Length

2025-11-06T20:13:28.660971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-06T20:13:28.758738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
los270
13.3%
angeles-long270
13.3%
beach-anaheim270
13.3%
ca270
13.3%
dallas-fort190
9.4%
worth-arlington190
9.4%
tx190
9.4%
phoenix-mesa-chandler150
7.4%
az150
7.4%
memphis39
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e1569
 
8.1%
n1490
 
7.6%
1379
 
7.1%
-1298
 
6.7%
o1260
 
6.5%
a1220
 
6.3%
A1189
 
6.1%
h1069
 
5.5%
l990
 
5.1%
s919
 
4.7%
Other values (24)7100
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)19483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1569
 
8.1%
n1490
 
7.6%
1379
 
7.1%
-1298
 
6.7%
o1260
 
6.5%
a1220
 
6.3%
A1189
 
6.1%
h1069
 
5.5%
l990
 
5.1%
s919
 
4.7%
Other values (24)7100
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1569
 
8.1%
n1490
 
7.6%
1379
 
7.1%
-1298
 
6.7%
o1260
 
6.5%
a1220
 
6.3%
A1189
 
6.1%
h1069
 
5.5%
l990
 
5.1%
s919
 
4.7%
Other values (24)7100
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1569
 
8.1%
n1490
 
7.6%
1379
 
7.1%
-1298
 
6.7%
o1260
 
6.5%
a1220
 
6.3%
A1189
 
6.1%
h1069
 
5.5%
l990
 
5.1%
s919
 
4.7%
Other values (24)7100
36.4%

Interactions

2025-11-06T20:13:23.375836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.109209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.237209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:16.287148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:17.814692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:19.972270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.166042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.297666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:23.519096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.292425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.372112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:16.430862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:18.005640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:20.167084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.302356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.431447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:23.661263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.431613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.494155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:16.622158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:18.297472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:20.336132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.435683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.566879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:23.809713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.567222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.630857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:16.838019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:18.513678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:20.511739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.572531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.721225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:23.950190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.707072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.757251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:17.021878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:18.840121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:20.652239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.714602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.852196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:24.077785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.837196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.893309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:17.166957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:19.210665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:20.783238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.844744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.985891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:24.207016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:14.967036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:16.037123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:17.317268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:19.472011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:20.911056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.972059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:23.118369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:24.337960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:15.098844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:16.157035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:17.617865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:19.762811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:21.030178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:22.130211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-06T20:13:23.248221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-06T20:13:28.900933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ZCTA5CEcommute_min_proxylong45_sharelong60_sharemetro_keymetro_namerent_to_incomestops_per_km2ttw_totalzori
ZCTA5CE1.0000.3550.3290.3420.9990.9990.2560.4410.0710.731
commute_min_proxy0.3551.0000.9930.9740.3650.3650.003-0.0930.0040.462
long45_share0.3290.9931.0000.9520.3460.346-0.020-0.108-0.0080.453
long60_share0.3420.9740.9521.0000.3450.3450.004-0.1060.0070.440
metro_key0.9990.3650.3460.3451.0001.0000.2030.1880.0940.424
metro_name0.9990.3650.3460.3451.0001.0000.2030.1880.0940.424
rent_to_income0.2560.003-0.0200.0040.2030.2031.0000.4280.1750.009
stops_per_km20.441-0.093-0.108-0.1060.1880.1880.4281.0000.1690.246
ttw_total0.0710.004-0.0080.0070.0940.0940.1750.1691.000-0.129
zori0.7310.4620.4530.4400.4240.4240.0090.246-0.1291.000

Missing values

2025-11-06T20:13:24.548642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-06T20:13:24.723622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-06T20:13:24.979302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ZCTA5CErent_to_incomelong45_sharelong60_sharecommute_min_proxyttw_totalperiodzoristops_per_km2metro_keymetro_name
0850030.2913750.0688530.0593115.5133592788.02025-09-301781.14215717.295065phoenixPhoenix-Mesa-Chandler, AZ
1850040.3822020.0964540.0653026.5175245738.02025-09-301824.10687818.415723phoenixPhoenix-Mesa-Chandler, AZ
2850060.2439990.0502740.0337013.6149259884.02025-09-301438.69562812.964971phoenixPhoenix-Mesa-Chandler, AZ
3850070.2558010.1063250.0784567.7468455308.02025-09-301450.00264611.023520phoenixPhoenix-Mesa-Chandler, AZ
4850080.2645480.0763280.0529865.71792429411.02025-09-301457.8584836.127955phoenixPhoenix-Mesa-Chandler, AZ
5850090.3042690.1211180.0721098.29177618879.02025-09-301538.6111115.657946phoenixPhoenix-Mesa-Chandler, AZ
6850120.2259600.1025540.0744987.3415297638.02025-09-301580.38916716.703535phoenixPhoenix-Mesa-Chandler, AZ
7850130.2253630.0837380.0475965.9719264383.02025-09-301609.62608813.120349phoenixPhoenix-Mesa-Chandler, AZ
8850140.2279570.0698580.0555435.16138013110.02025-09-301545.96192412.362612phoenixPhoenix-Mesa-Chandler, AZ
9850150.3064540.1047750.0646767.30939321158.02025-09-301253.54435810.399347phoenixPhoenix-Mesa-Chandler, AZ
ZCTA5CErent_to_incomelong45_sharelong60_sharecommute_min_proxyttw_totalperiodzoristops_per_km2metro_keymetro_name
639762260.1951820.2105050.15387814.94639220553.02025-09-302947.5375000.000000dallasDallas-Fort Worth-Arlington, TX
640762270.2210720.3098310.25294822.79817422320.02025-09-301810.5204820.000000dallasDallas-Fort Worth-Arlington, TX
641762440.2298710.1789400.13120812.68053927329.02025-09-302108.9422300.181381dallasDallas-Fort Worth-Arlington, TX
642762470.1738870.2998850.23064621.47701411163.02025-09-302214.0391030.000000dallasDallas-Fort Worth-Arlington, TX
643762480.2388640.1922000.11155412.93216711253.02025-09-301963.1941520.000000dallasDallas-Fort Worth-Arlington, TX
644762490.1858550.2598310.18583618.2114683448.0NaTNaN0.000000dallasDallas-Fort Worth-Arlington, TX
645762580.2086100.3688710.21265724.4857471754.02025-09-301956.9166670.000000dallasDallas-Fort Worth-Arlington, TX
646762590.2560500.2339930.23399317.549476859.0NaTNaN0.000000dallasDallas-Fort Worth-Arlington, TX
647762620.1697590.1760570.13110612.43689420415.02025-09-301744.5704460.000000dallasDallas-Fort Worth-Arlington, TX
648762660.1793540.1844980.14129113.38535212616.02025-09-301996.2777780.000000dallasDallas-Fort Worth-Arlington, TX